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Will the Customers Be Happy? Identifying Unsatisfied Customers from Service Encounter Data

Baier, Lucas; Kühl, Niklas; Schüritz, Ronny; Satzger, Gerhard

Abstract:
While the understanding of customer satisfaction is a key success factor for service enterprises, existing elicitation approaches suffer from several drawbacks such as high manual effort or delayed availability. However, the rise of analytical methods allows for the automatic and instant analysis of encounter data captured during service delivery in order to identify unsatisfied customers.
Based on encounter data of 1,584 IT incidents in a real-world service use case, supervised machine learning models to predict unsatisfied customers are trained and evaluated.
We show that the identification of unsatisfied customers from encounter data is well feasible: Via a logistic regression approach, we predict dissatisfied customers already with decent accuracy—a substantial improvement to the current situation of “flying blind”. In addition, we are able to quantify the impacts of key service elements on customer satisfaction.
The possibility to understand the relationship between encounter data and customer satisfaction will offer ample opportunities to evaluate and expand existing service management theories.
Identifying dissatisfied customers from encounter data adds a valuable methodology to customer service management. ... mehr



Originalveröffentlichung
DOI: 10.1108/JOSM-06-2019-0173
Dimensions
Zitationen: 1
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Karlsruhe Service Research Institute (KSRI)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 08.02.2021
Sprache Englisch
Identifikator ISSN: 1757-5818, 1757-5826
KITopen-ID: 1000119941
Erschienen in Journal of service management
Verlag Emerald
Band 32
Heft 2
Seiten 265 - 288
Vorab online veröffentlicht am 02.07.2020
Nachgewiesen in Dimensions
Scopus
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